The present invention is generally related to travel purchasing, and more particularly to an adaptive, dynamic travel purchasing optimization system for calculating and providing a process for reducing the total cost of business travel at the point of purchase.
Corporate travel management is a complicated task and the corporate travel industry currently uses a variety of travel software packages to implement travel purchasing. Current software packages aimed at the corporate travel industry, such as direct booking (under the trade names Sabre BTS, Internet Travel Network, etc.) and expense report processing systems, focus on increasing the efficiency of overhead costs and administrative processes. However, they have not addressed the major cost associated with the travel processxe2x80x94the cost of the actual travel. The cost of travel represents over 95% of total travel costs.
Unfortunately, corporations are unable to control travel costs because they do not have an effective way to analyze the options available to them at the point of purchase. In the corporate travel environment, agents and travelers have only static pre-set policies to guide them in a very dynamic marketplace. Currently, the major factor used by corporations to differentiate travel choices is price. However, corporations are unable to calculate the true price associated with each travel event due to the inaccuracy of fare cost calculation and the incomplete analyses of costs associated with an individual trip.
Inaccuracy of fare cost calculation stems from current systems that fail to calculate and display all of the dynamic options associated with each trip event. Corporations are unable to control travel costs, because they do not have an effective way to analyze the multitude of options available to them at the point of purchase. In the corporate travel environment, agents and travelers have only static pre-set policies to guide them in a very dynamic marketplace.
Currently, the major criterion used by corporations to differentiate travel choices is price or the xe2x80x9ccheapest fare.xe2x80x9d Unfortunately, corporations are unable to calculate the true price associated with each travel event, due to static viewing of dynamic options that can equal as much as 15 percent of the total fare cost. Dynamic options include fare discounts and negotiated pricing, among others. Thus, statically viewing the travel market leads to the selection of trips which may have one desirable element, such as, cheapest fare, but are actually significantly costlier than necessary due to other dynamic factors.
For example, hypothetical corporate traveler Smith needs a flight between Cleveland and Atlanta. Smith""s corporate travel agent, using an existing travel purchasing system, looks within a two hour departure window for the flight and finds the cheapest fare, according to company policy. However, there is a problem with the static policy-dedicated decision based on the cheapest fare. The existing travel purchasing system failed to reflect the overrides and back-end discounts negotiated by the company. Another flight, that is fifty dollars more according to the existing travel purchasing system, actually includes seventy-five dollars in discounts, making it the optimal flight from a dynamic cost perspective.
Incompleteness of cost calculation stems from current systems that have no way of calculating variables associated with each trip event that are even more dynamic and less tangible than price. These more dynamic and less tangible variables include costs created by differences in travel time, the productivity impact of inconvenience, the probability that lower priced choices will become available in the near future, and the impact of each choice on the ability of the airline to negotiate price discounts or bulk purchases with suppliers. In the current travel environment, no known solution addresses all of these variables, resulting in needlessly increased expenditures and reduced productivity.
Pointedly, travel policies, the most popular current cost-control method, are ineffective without an understanding of the true costs involved for each trip choice. Business travelers and their agents do not have the time, training, incentives or tools to analyze the variables. Without direct corporate intervention at the point of purchase, companies essentially rely on pure luck to ensure that agents and travelers are selecting the best trip options.
Referring back to the previous hypothetical example, corporate traveler Smith again needs a flight between Cleveland and Atlanta. Smith""s corporate travel agent, using an existing travel purchasing system, looks within a two hour departure window for the flight and finds the cheapest fare, according to company policy. However, the existing travel purchasing system did not account for Smith""s salary of seventy-five dollars per hour and Smith""s preference for short trips due to his asthma. Another flight, that is fifty dollars more according to the existing travel purchasing system, has both a seventy-five dollar back-end discount negotiated by the company and no layover, reducing the fare by twenty-five dollars and saving Smith two hours in travel time. As a result of using a static policy-based travel purchasing system, the company lost twenty-five dollars in overall fare, lost 150 dollars in travel time for Smith""s salary, and lost employee morale by putting Smith on a longer flight.
Known automated travel planners depend upon standard crisp logic decisions, basically following a series of decisions based upon crisp xe2x80x9cYes or Noxe2x80x9d decisions without appropriately taking into account qualitative considerations such as productivity impact of travel, negotiated contract compliance, inconvenience of traveler, airline affinity or loyalty, employee morale, frequent flyer miles, and airline policies. Such qualitative considerations may easily vary in priority from trip to trip, business to business, traveler to traveler and even day to day. Capturing varying degrees of significance of many such qualitative considerations in a conventional crisp algorithm based computer program would be a daunting programming task.
Further, such conventional systems are not easily adaptable or tunable to a particular user. Tuning or adapting such a system to changing conditions typically may be carried out by changing the algorithm. Changing an algorithm requires editing program steps and recompiling the necessary code. Essentially the program must be rewritten in order to modify preferences or change conditions. This is a time consuming and non-productive use of programming personnel.
By way of background, one example of a known system may be found in U.S. Pat. No. 5,331,546, to Webber et al., entitled xe2x80x9cTRIP PLANNER OPTIMIZING TRAVEL ITINERARY SELECTION CONFORMING TO INDIVIDUALIZED TRAVEL POLICIES,xe2x80x9d issued Jul. 19, 1994. The entire contents of U.S. Pat. No. 5,331,546 are incorporated by reference into this patent application. Another example is U.S. Pat. No. 5,832,453 to O""Brien issued Nov. 3, 1998 and entitled xe2x80x9cCOMPUTER SYSTEM AND METHOD FOR DETERMINING A TRAVEL SCHEME MINIMIZING TRAVEL COSTS FOR AN ORGANIZATION.xe2x80x9d The entire contents of U.S. Pat. No. 5,832,453 are incorporated by reference into this patent application.
In contrast to the prior art, one embodiment of the travel purchasing optimization system (TPOS) of the invention comprises the first solution focused specifically on optimizing corporate travel decisions at the point of purchase using the elegance and power of fuzzy membership functions. The invention is based on the concept that corporations already possess enough information to efficiently calculate the true cost of travel. The invented travel purchasing optimization system collects available information, brings it to the point of purchase, and analyzes it for the various choices available in the marketplace using a process referred to as dynamic optimization. Dynamic optimization synthesizes qualitative and quantitative components associated with the total cost of travel. The quantitative components include, but are not limited to, commission refunds, negotiated discounts, overrides, and cost of travel time. The qualitative components include, but are not limited to, productivity impact of travel, negotiated contract compliance, inconvenience of traveler, airline affinity or loyalty, employee morale, frequent flyer miles, and airline policies. Qualitative components are advantageously represented using fuzzy membership functions.
To dynamically optimize decision making at the point of purchase, the invented travel purchasing optimization system uses leading edge fuzzy logic based computational intelligence techniques to calculate the total cost of travel by weighing monetary and non-monetary variables. The invented travel purchasing optimization system collects pertinent information from existing enterprise software systems and data repositories to analyze the available choices. It calculates the total cost of travel (TCOT) for each of the choices available to a traveler, allowing the corporation to enforce lowest total cost purchases.
From an economic standpoint the total cost of travel (TCOT) includes any variable that impacts the desirability of a particular choice. Fuzzy logic based computational intelligence makes it easier to model the human thought process and to deal with systems where a clear mathematical model is not known, such as, calculation of the TCOT. For example, any traveler can tell you that xe2x80x9call 500 dollar tickets are not the same,xe2x80x9d since there are different xe2x80x9ccosts,xe2x80x9d beyond price, that reflect the value of the ticket; however, existing travel purchasing systems could not differentiate the value of the two 500 dollar tickets. Therefore, the invented travel purchasing optimization system holistically analyzes travel choices by examining variables that reduce the direct costs of travel, while ensuring that indirect costs, such as convenience and service, are not excessively compromised.
It is an object of the present invention to provide an accurate and complete travel purchasing optimization system for calculating the total cost of travel with a reduced rule set at the point of purchase.
It is another object of the present invention to increase accuracy in the calculation of the total cost of travel by processing qualitative and quantitative factors.
It is an object of the present invention to provide a modeling language to represent qualitative and quantitative factors in terms of linguistic variables used for calculating the total cost of travel using fuzzy logic based computational intelligence.
In accordance with the present invention, a travel purchasing optimization system comprises a cognizer in communication with a knowledge base and a rule base. A knowledge acquisition and rule manager (KARM) dynamically creates, alters, and fine tunes rules for the rule base from existing business knowledge using Yatra description language. A travel request is made through an external booking system and sent to the cognizer via an integration framework and the knowledge base. The integration framework pools data from a series of external systems or databases. The cognizer compares the rules from the rule base with the pooled data from the integration framework. The cognizer generates the total cost of travel (TCOT) and sends the TCOT back to the external booking system via the integration framework at the point of purchase. Other objects, features and advantages of the present invention will become apparent to those skilled in the art through the description of the preferred embodiment, claims and drawings wherein like numerals refer to like elements.